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Gouel P, Hapdey S, Dumouchel A, Gardin I, Torfeh E, Hinault P, Vera P, Thureau S, Gensanne D. Synthetic MRI for Radiotherapy Planning for Brain and Prostate Cancers: Phantom Validation and Patient Evaluation. Front Oncol 2022; 12:841761. [PMID: 35515105 PMCID: PMC9065558 DOI: 10.3389/fonc.2022.841761] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 03/15/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose We aimed to evaluate the accuracy of T1 and T2 mappings derived from a multispectral pulse sequence (magnetic resonance image compilation, MAGiC®) on 1.5-T MRI and with conventional sequences [gradient echo with variable flip angle (GRE-VFA) and multi-echo spin echo (ME-SE)] compared to the reference values for the purpose of radiotherapy treatment planning. Methods The accuracy of T1 and T2 measurements was evaluated with 2 coils [head and neck unit (HNU) and BODY coils] on phantoms using descriptive statistics and Bland–Altman analysis. The reproducibility and repeatability of T1 and T2 measurements were performed on 15 sessions with the HNU coil. The T1 and T2 synthetic sequences obtained by both methods were evaluated according to quality assurance (QA) requirements for radiotherapy. T1 and T2in vivo measurements of the brain or prostate tissues of two groups of five subjects were also compared. Results The phantom results showed good agreement (mean bias, 8.4%) between the two measurement methods for T1 values between 490 and 2,385 ms and T2 values between 25 and 400 ms. MAGiC® gave discordant results for T1 values below 220 ms (bias with the reference values, from 38% to 1,620%). T2 measurements were accurately estimated below 400 ms (mean bias, 8.5%) by both methods. The QA assessments are in agreement with the recommendations of imaging for contouring purposes for radiotherapy planning. On patient data of the brain and prostate, the measurements of T1 and T2 by the two quantitative MRI (qMRI) methods were comparable (max difference, <7%). Conclusion This study shows that the accuracy, reproducibility, and repeatability of the multispectral pulse sequence (MAGiC®) were compatible with its use for radiotherapy treatment planning in a range of values corresponding to soft tissues. Even validated for brain imaging, MAGiC® could potentially be used for prostate qMRI.
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Affiliation(s)
- Pierrick Gouel
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France
| | - Sebastien Hapdey
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France
| | - Arthur Dumouchel
- Imaging Department, Henri Becquerel Cancer Center, Rouen, France
| | - Isabelle Gardin
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France.,Radiotherapy Department, Henri Becquerel Cancer Center, Rouen, France
| | - Eva Torfeh
- Radiotherapy Department, Henri Becquerel Cancer Center, Rouen, France
| | - Pauline Hinault
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France
| | - Pierre Vera
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France
| | - Sebastien Thureau
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France.,Radiotherapy Department, Henri Becquerel Cancer Center, Rouen, France
| | - David Gensanne
- Quantification en Imagerie Fonctionnelle-Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes Equipe d'accueil 4108 (QuantIF-LITIS EA4108), University of Rouen, Rouen, France.,Imaging Department, Henri Becquerel Cancer Center, Rouen, France.,Radiotherapy Department, Henri Becquerel Cancer Center, Rouen, France
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Li Z, Srivastava SP, Karis JP. Technical note: A spiral fluid-attenuated inversion recovery magnetic resonance imaging technique for stereotactic radiosurgery treatment planning for trigeminal neuralgia. Med Phys 2021; 48:6881-6888. [PMID: 34628668 DOI: 10.1002/mp.15271] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 09/18/2021] [Accepted: 09/21/2021] [Indexed: 12/21/2022] Open
Abstract
PURPOSE Magnetic resonance imaging (MRI) is commonly used in treatment planning for stereotactic radiosurgery (SRS) of trigeminal neuralgia (TN). With current MRI techniques, the delineation of the trigeminal nerve root entry zone (REZ) may be degraded due to poor contrast and artifacts. The purpose of this work is to develop an MRI technique with better delineation of the trigeminal nerve REZ to improve SRS treatment planning for TN. METHODS A spiral fluid-attenuated inversion recovery (FLAIR) MRI technique was developed to improve image quality by improving tissue contrast, fluid suppression, artifact reduction, and signal-to-noise ratio (SNR). A concomitant-phase compensation method based on spiral gradient waveforms was implemented to minimize artifacts due to magnetic field change induced by the metal frame used in Gamma Knife treatment planning. The image quality of spiral FLAIR was assessed in four healthy volunteers. The geometric accuracy was quantitatively evaluated by registering spiral FLAIR to computed tomography (CT) images and comparing it with existing MRI techniques. RESULTS The spiral FLAIR technique demonstrated better delineation of the trigeminal nerve REZ, improved tissue contrast of the brain stem, and minimized flow artifacts, compared to steady-state free precession (SSFP) MRI. Spiral FLAIR also improved fluid suppression, SNR, and artifacts, which contributed to better delineation of the trigeminal nerve REZ compared to conventional Cartesian FLAIR. The measured mean (± standard deviation) distance between spiral FLAIR and CT images is 0.98 ± 0.56 mm, comparable to 0.40 ± 0.26 mm in 3T T1 spoiled gradient echo (T1-SPGR), 0.59 ± 0.25 mm in 3T SSFP, 0.66 ± 0.38 mm in 1.5T T1-SPGR, and 0.61 ± 0.25 mm in 1.5T Cartesian FLAIR. CONCLUSION A spiral FLAIR technique with improved image quality and good geometric accuracy provides a potential alternative for treatment planning in SRS for TN patients.
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Affiliation(s)
- Zhiqiang Li
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Shiv P Srivastava
- Department of Radiation Oncology, Dignity Health Cancer Institute, St Joseph's Hospital and Medical Center, Phoenix, Arizona, USA
| | - John P Karis
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, Arizona, USA
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Calusi S, Arilli C, Mussi E, Puggelli L, Farnesi D, Casati M, Compagnucci A, Marrazzo L, Talamonti C, Zani M, Pallotta S. In phantom evaluation of targeting accuracy in MRI-based brain radiosurgery. Phys Med 2021; 85:158-164. [PMID: 34015617 DOI: 10.1016/j.ejmp.2021.05.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 04/06/2021] [Accepted: 05/08/2021] [Indexed: 01/21/2023] Open
Abstract
PURPOSE To determine the targeting accuracy of brain radiosurgery when planning procedures employing different MRI and MRI + CT combinations are adopted. MATERIALS AND METHOD A new phantom, the BrainTool, has been designed and realized to test image co-registration and targeting accuracy in a realistic anatomical situation. The phantom was created with a 3D printer and materials that mimic realistic brain MRI and CT contrast using a model extracted from a synthetic MRI study of a human brain. Eight markers distributed within the BrainTool provide for assessment of the accuracy of image registrations while two cavities that host an ionization chamber are used to perform targeting accuracy measurements with an iterative cross-scan method. Two procedures employing 1.5 T MRI-only or a combination of MRI (taken with 1.5 T or 3 T scanners) and CT to carry out Gamma Knife treatments were investigated. As distortions can impact targeting accuracy, MR images were preliminary evaluated to assess image deformation extent using GammaTool phantom. RESULTS MR images taken with both scanners showed average and maximum distortion of 0.3 mm and 1 mm respectively. The marker distances in co-registered images resulted below 0.5 mm for both MRI scans. The targeting mismatches obtained were 0.8 mm, 1.0 mm and 1.2 mm for MRI-only and MRI + CT (1,5T and 3 T), respectively. CONCLUSIONS Procedures using a combination of MR and CT images provide targeting accuracies comparable to those of MRI-only procedures. The BrainTool proved to be a suitable tool for carrying out co-registration and targeting accuracy of Gamma Knife brain radiosurgery treatments.
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Affiliation(s)
- S Calusi
- Department of Biomedical, Experimental and Clinical Sciences "Mario Serio", University of Florence, Italy; National Institute of Nuclear Physics, Florence, Italy.
| | - C Arilli
- Medical Physics Unit, AOU Careggi, Florence, Italy
| | - E Mussi
- Department of Industrial Engineering, University of Florence, Italy
| | - L Puggelli
- Department of Industrial Engineering, University of Florence, Italy
| | - D Farnesi
- CNR-IFAC, Institute of Applied Physics "N. Carrara", Florence, Italy
| | - M Casati
- Medical Physics Unit, AOU Careggi, Florence, Italy
| | | | - L Marrazzo
- Medical Physics Unit, AOU Careggi, Florence, Italy
| | - C Talamonti
- Department of Biomedical, Experimental and Clinical Sciences "Mario Serio", University of Florence, Italy; National Institute of Nuclear Physics, Florence, Italy; Medical Physics Unit, AOU Careggi, Florence, Italy
| | - M Zani
- Medical Physics Unit, AOU Careggi, Florence, Italy
| | - S Pallotta
- Department of Biomedical, Experimental and Clinical Sciences "Mario Serio", University of Florence, Italy; National Institute of Nuclear Physics, Florence, Italy; Medical Physics Unit, AOU Careggi, Florence, Italy
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Porter E, Fuentes P, Siddiqui Z, Thompson A, Levitin R, Solis D, Myziuk N, Guerrero T. Hippocampus segmentation on noncontrast CT using deep learning. Med Phys 2020; 47:2950-2961. [DOI: 10.1002/mp.14098] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 01/28/2020] [Accepted: 01/29/2020] [Indexed: 11/06/2022] Open
Affiliation(s)
- Evan Porter
- Department of Medical Physics Wayne State University Detroit MI USA
- Beaumont Artificial Intelligence Research Laboratory Beaumont Health Systems Royal Oak MI USA
- Department of Radiation Oncology Beaumont Health Systems Royal Oak MI USA
| | - Patricia Fuentes
- Beaumont Artificial Intelligence Research Laboratory Beaumont Health Systems Royal Oak MI USA
- Oakland University William Beaumont School of Medicine Oakland University Rochester MI USA
| | - Zaid Siddiqui
- Beaumont Artificial Intelligence Research Laboratory Beaumont Health Systems Royal Oak MI USA
- Department of Radiation Oncology Beaumont Health Systems Royal Oak MI USA
| | - Andrew Thompson
- Beaumont Artificial Intelligence Research Laboratory Beaumont Health Systems Royal Oak MI USA
- Department of Radiation Oncology Beaumont Health Systems Royal Oak MI USA
| | - Ronald Levitin
- Beaumont Artificial Intelligence Research Laboratory Beaumont Health Systems Royal Oak MI USA
- Department of Radiation Oncology Beaumont Health Systems Royal Oak MI USA
| | - David Solis
- Beaumont Artificial Intelligence Research Laboratory Beaumont Health Systems Royal Oak MI USA
- Department of Radiation Oncology Beaumont Health Systems Royal Oak MI USA
| | - Nick Myziuk
- Beaumont Artificial Intelligence Research Laboratory Beaumont Health Systems Royal Oak MI USA
- Department of Radiation Oncology Beaumont Health Systems Royal Oak MI USA
| | - Thomas Guerrero
- Beaumont Artificial Intelligence Research Laboratory Beaumont Health Systems Royal Oak MI USA
- Department of Radiation Oncology Beaumont Health Systems Royal Oak MI USA
- Oakland University William Beaumont School of Medicine Oakland University Rochester MI USA
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